COGNITIVE RADIO FOR NEXT-GENERATION WIRELESS NETWORKS: AN APPROACH TO OPPORTUNISTIC CHANNEL SELECTION IN IEEE 802.11-BASED WIRELESS MESH

Dusit Niyato, Nanyang Technological UniversityEkram Hossain, University of ManitobaIEEE Wireless Communication Feb. 2009

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Outline

Introduction Cognitive Radio

Basic Components, Approaches In Different Wireless Systems Research Issues in Protocol Design

An Approach to Opportunistic Channel Selection in IEEE 802.11-Based Wireless Mesh System Model Dynamic Opportunistic Channel Selection Scheme Performance Evaluation

Conclusion Comments

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Introduction3

Frequency spectrum is the scarcest resource for wireless communications may become congested to accommodate diverse types

of air interfaces in next-generation wireless networks Software radio

Improves the capability of a wireless transceiver by using embedded software

Enable the radio transceiver to operate in multiple frequency bands

Cognitive radio A special type of software defined radio Able to intelligently adapt itself to the changing

environment

Cognitive Radio4

Basic Components Observation Process

Measurement and noise reduction mechanism Passive observation

The radio transceiver silently listens to the environment.

Active observation Special messages or signals are transmitted and

measured to obtain information about the surrounding environment

Learning Process Extract useful information from collected data Reinforcement learning algorithm

is used when the correct solution is unknown Learning through interactions

Cognitive Radio (cont’d)5

Planning and Decision Making Process Using knowledge obtained from learning to

schedule and prepare for the next transmission A transceiver must decide to choose the best

strategy to achieve the target objective Action

The action of a transceiver is controlled by the planning and decision making process

Cognitive Radio (cont’d)6

Approaches Estimation Technique

Obtain information about the ambient network environment

Game Theory Evolutionary Computation

Genetic algorithm Fuzzy Logic Markov Decision Process Pricing Theory Theory of Social Science Reinforcement Learning

Cognitive Radio (cont’d)7

In different wireless systems IEEE 802.11 and 802.16 Networks

May operate in the same unlicensed frequency band Efficient spectrum management and planning are

required IEEE 802.22 Networks (WRANs)

The first wireless communication standard adopting intelligent software defined radio

Ultra Wideband-based (WPANs) Cooperative Diversity Wireless Networks

Primary users and secondary users

Cognitive Radio (cont’d)8

Research issues in protocol design Lightweight and cooperative protocols for

cognitive radio networks Battery-limited, energy consumption for the

execution of estimation, learning, and decision making algorithm should be minimized

Cross-layer optimization in cognitive radio networks To optimize QoS performance in a cognitive

radio network

Dynamic Channel Selection Scheme

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In the proposed scheme A wireless node/mesh client learns physical

(i.e., signal strength) and MAC layer (i.e., collision probability)

Accordingly selects the best channel to connect to a mesh router

The decision can be made independently in each node in a distributed manner by using an intelligent algorithm

Dynamic Channel Selection Scheme (cont’d)

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System Model IEEE 802.11 Mesh 100m*100m No centralized controller

Dynamic Channel Selection Scheme (cont’d)

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Pc(f): collision probability on channel f estimate the amount of traffic load γf: estimated signal strength

Fuzzy logic controller

Dynamic Channel Selection Scheme (cont’d)

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Wireless node utility The decision on dynamic channel selection

at each node is based on utility function of collision probability Pc(f) and received signal strength γf on channel f.

Both collision probability and received signal strength impact the throughput and error performances experienced by a wireless node.

Dynamic Channel Selection Scheme (cont’d)

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Fuzzy logic Use “collision probability” as an indicator of

traffic load in each channel The interference rules are used to gain

information on the traffic load condition in a channel

Example:Result utility

Estimated collision prob.

Dynamic Channel Selection Scheme (cont’d)

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Let mf,i denote the membership function for channel f obtained from fuzzification.

This mf,i can be obtained using a standard fuzzification method.

Then the fitness of rule k to the traffic load condition can be obtained from if

Ffk mM ,1

The estimated utility

The normalized fitness

Dynamic Channel Selection Scheme (cont’d)

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Learning algorithm is used to approximate the utility Ui,f,k

perceived by each wireless node corresponding to the different traffic load condition in the service area

α: the learning rateUold

i,f,k: the utility of the previous learning iteration

Dynamic Channel Selection Scheme (cont’d)

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Decision on Channel Selection Wireless node i chooses channel that provides

the highest Ui,f

This channel selection is executed periodically. The decision can be made if the estimated

collision probability and received signal strength change by an amount larger than the predefined thresholds, which implies that one or more new nodes are

accessing the channel and/or some nodes have terminated connections with the corresponding mesh router.

Dynamic Channel Selection Scheme (cont’d)

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Performance Evaluation Each router operates in DCF mode For the channel selection scheme we set α:

0.1, and it is executed at each node periodically every 2 min.

Using MATLAB to run the time-driven simulation

Dynamic Channel Selection Scheme (cont’d)

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Wireless nodes and the associated mesh routers: a) at time 0; b) after 30 minutes

Dynamic Channel Selection Scheme (cont’d)

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a) Wireless nodes and the associated mesh routers (for non-uniform node distribution)

b) Variation in average node throughput

Dynamic Channel Selection Scheme (cont’d)

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Effect of uniformity of node distribution on the network utility

Conclusion21

An overview of the difference components in cognitive radio and the related approaches have been presented

The dynamic channel selection for opportunistic spectrum access in IEEE 802.11-based multichannel wireless mesh networks

It performs significantly better than some of the traditional schemes, especially with non-uniform node distribution in the service area.

Comments22

Provide an introduction to cognitive radio’s approaches.

Learning rate selection is a issue. Convergence and performance

Comparison with other channel selection scheme?